Search Results for author: Matteo Chinazzi

Found 9 papers, 6 papers with code

Disentangled Multi-Fidelity Deep Bayesian Active Learning

1 code implementation7 May 2023 Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication.

Active Learning Gaussian Processes

Multi-fidelity Hierarchical Neural Processes

1 code implementation10 Jun 2022 Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.

Epidemiology Gaussian Processes

Deep Bayesian Active Learning for Accelerating Stochastic Simulation

1 code implementation5 Jun 2021 Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.

Active Learning

Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

no code implementations21 Jun 2020 Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu

% We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

1 code implementation8 Apr 2020 Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T Davis, Alessandro Vespignani, Mauricio Santillana

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time.

BIG-bench Machine Learning Clustering +2

Inferring high-resolution human mixing patterns for disease modeling

1 code implementation25 Feb 2020 Dina Mistry, Maria Litvinova, Ana Pastore y Piontti, Matteo Chinazzi, Laura Fumanelli, Marcelo F. C. Gomes, Syed A. Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M. Elizabeth Halloran, Ira M. Longini Jr., Stefano Merler, Marco Ajelli, Alessandro Vespignani

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics.

Populations and Evolution Physics and Society Quantitative Methods

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